Knowledge, practices, and environmental health risks associated with electronic waste recycling in Cotonou, Benin
Bibliographic record
Abstract
The recycling of e-waste is increasing rapidly worldwide and there remain outstanding environmental health concerns. However, most studies are localized to few countries (e.g., China, Ghana). This study analyzes the knowledge and practices of e-waste recyclers in Cotonou from which a deeper understanding of environmental health risks could be determined. A descriptive, cross-sectional study was conducted in September 2018. All e-waste recyclers working in Cotonou, having given their consent and available during the investigation period were interviewed individually. Survey data was collected from 45 recyclers concern their professional profile, knowledge of the risks of their activities on health and environment and their daily recycling practices. The data analysis was done under the SPSS software and the graphs were generated under Microsoft Excel. All of the 45 people were male. The average age is 24 ± 6 years old and 53.3% of recyclers have at least 3 years of seniority. Recyclers dismantle (97.8%), sort (91.1%) and incinerate (88.9%) e-waste. Only 44.2% of recyclers wear at least one piece of personal protective equipment and 48.8% do not wash their hands before eating at recycling sites. More than 90% noted that their residues are abandoned in nature and 46.7% think that e-waste can pollute water against 71.1% for air and soil. Regarding the diseases that can be linked to their activity, recyclers self-recognize respiratory diseases 67.4%, heart diseases 62.8%, eye diseases 65.1%, kidney diseases 41.9% and cancers 30.2%.Note that the number of e-waste dismantled per month is significantly associated with the symptoms experienced: blood in the urine and stool, wounds, dizziness, itchy skin. The number of hours of work per day is associated with: blood in the urine, dizziness, itchy skin and airway obstruction. It becomes important to raise awareness of e-waste workers about the dangers of their activities and encourage prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".